Mechanistic Modeling
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Related Articles from SNS
Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework
arXiv:2602.18008v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promise in constructing mechanistic models from data. However, existing evaluations largely focus on simplified settings and fail to capture the complexity of real-world scientific modeling. In practice, such modeling often involves neural-integrated formulations, where a mechanistic model component and a neural network component are jointly constructed, leading to a significantly more complex search...
MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models
Identifying robust biomarkers from high-dimensional biomedical data is a central challenge in translational research, but candidate rankings produced by any single feature-selection or classification method depend on algorithmic choices and rarely reproduce across pipelines. We present a disease-agnostic machine-learning framework that addresses this dependence by systematically benchmarking 25 (feature-selection x classifier) pipelines under five-fold stratified cross-validation,...
Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming
Announce Type: new Abstract: Advances in computational modeling, neuroimaging, and artificial intelligence are revolutionizing the modeling of neurological disorders for improved diagnostics, prognosis, and treatment planning. Mechanistic models provide valuable scientific insight into the disorders, but in practice they are often simplified with assumptions or computationally expensive and slow to solve. However, while purely data driven approaches provide speed and scalability, they...
Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming
Announce Type: cross Abstract: Advances in computational modeling, neuroimaging, and artificial intelligence are revolutionizing the modeling of neurological disorders for improved diagnostics, prognosis, and treatment planning. Mechanistic models provide valuable scientific insight into the disorders, but in practice they are often simplified with assumptions or computationally expensive and slow to solve. However, while purely data driven approaches provide speed and scalability, they...
When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models
arXiv:2606.03712v1 Announce Type: new Abstract: Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks. By transforming graph topology and node information into graph tokens, GLMs allow LLMs to jointly process structured graph inputs and textual instructions. Yet, it remains unclear how LLMs internally interpret these graph tokens and whether graph tokens act as meaningful carriers of graph structure.
The Mechanistic Emergence of Symbol Grounding in Language Models
arXiv:2510.13796v3 Announce Type: replace Abstract: Symbol grounding (Harnad, 1990) describes how symbols such as words acquire their meanings by connecting to real-world sensorimotor experiences. Recent work has shown preliminary evidence that grounding may emerge in (vision-)language models trained at scale without using explicit grounding objectives. Yet, the specific loci of this emergence and the mechanisms that drive it remain largely unexplored.
Human Genome-Scale Models of Metabolism and Gene Expression Reveal Resource Constraints of Cancer Cell Lines
Genome-scale metabolic models (M-models) provide mechanistic insight into intracellular metabolism by simulating fluxes subject to nutrient and energy resource constraints. However, they cannot account for a major component of resource allocation, since they do not explicitly account for the cost of producing and maintaining enzymes. Genome-scale models of metabolism and gene expression (ME-Models) address this by including gene expression reactions, but these have only been developed for...
Pattern Selectivity is Not Task-Causal Structure: A Cross-Architecture Mechanistic Study of Composed-Task Circuits in 1B-Class Language Models
arXiv:2606.05378v1 Announce Type: new Abstract: We test whether a single screen-and-ablate recipe -- identify attention-head circuits by task-pattern selectivity, then verify by causal ablation against a matched-random null -- produces consistent mechanistic claims across model families. The recipe ports across pipelines; the specific circuit it identifies does not. Across four composed tasks (indirect-object identification, greater-than, successor sequences, variable binding) and three...
How Language Models Process Negation
Announce Type: replace Abstract: We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong answers to questions involving negation, they do possess internal components that process negation correctly. Their poor accuracy is due to late-layer attention behavior that promotes simple shortcuts; ablating those attention modules greatly improves accuracy on negation-related questions.
Correcting Gradient-Based Circuit Localization via Interaction-Aware Backpropagation
arXiv:2505.17630v4 Announce Type: replace Abstract: Circuit localization methods aim to identify the subset of model components responsible for specific behaviors in large language models, enabling detailed mechanistic analysis. Most existing methods assume components act independently and estimate importance by perturbing each component in isolation. However, components in neural networks interact, and ignoring these interactions leads to systematic misestimation of component importance.